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Identify Problems with Artificial Intelligence – Case Study

Industry 4.0: Bring your Complex Problem Solving Skills to a new level. Contains Deep Learning Tutorial.
Vladimir Zhbanko
167 students enrolled
English More
Identify anomaly within several similar objects
Apply Unsupervised Machine Learning algorithm kmeans
Develop and deploy ShinyApp
Apply Version Control to your projects or activities
Re-use provided template and course exercises in R and ShinyApp
Use Deep Learning Autoencoder Models to Detect Anomalies in Time-Series data
Create a System that Supervises Industrial Process and helps Process Operators to detect anomalies

Inspired by Albert Einstein [1879-1955]

Course summary:

  • Learn how to identify anomaly within several similar objects with Artificial Intelligence

  • Working with time-series sensor generated data

  • Understand how Unsupervised Machine Learning Algorithm works using real life dataset

  • Learn developing in R and ShinyApp with a possibility to better explore the data, instantly deploy your project

  • Explained use of Version Control to be organized and save time

  • Practice with real life generalized Dataset coming from Manufacturing!

  • Versatile method is presented using a Case Study approach.

  • This method helped to discover real life inefficiency and to solve the problem!

  • Start with R here! Step by step introduction with examples and practice

  • Basic understanding on Time-Series data manipulation in R

  • More approaches of Anomaly Detection including Deep Learning on h2o framework is covered in the course

  • Practical Developing the idea of Industrial Process Control with Artificial Intelligence with DEMO Shiny Application included

  • Course video captions are translated to [Chinese-Simplified, Hindi, German, French, Italian, Portuguese, Turkish, Spanish, Malay, Indonesian, Russian] languages


Problem-solving in Manufacturing is usually perceived as a slow and boring activity especially when many possible factors involved. At the same time it’s often common that problems going on and on unobserved which is very costly. Is it possible to apply Artificial Intelligence to help human to identify the problem? Is it possible to dedicate this boring problem solving activity to computer? Apparently yes!!!
This course will help you to combine popular problem-solving technique called “is/is not” with Artificial Intelligence in order to quickly identify the problem.
We will use data coming from four similar Machines.  We will process it through the Unsupervised Machine Learning Algorithm k-means. Once you get intuition understanding how this system work You will be amazed to see how easy and versatile the concept is. In our project you will see that helped by Artificial Intelligence Human eye will easily spot the problem. 

Course will also exploit different other methods of Anomaly Detection. Probably the most interesting one is to use Deep Learning Autoencoders models built with help of H2O Platform in R.

Using collected data and Expert Knowledge for Process Control with AI:

In this course we will build and demo-try entire multi-variables process supervision system. Process Expert should select dataset coming from the ideally working process. Deep Learning model will be fit to that specific pattern. This model can be used to monitor the process as the new data is coming in. Anomaly in the process then can be easily detected by the process operators.

Ready for Production:

Another great value from the Course is the possibility to learn using ShinyApp. This tool will help you to instantly deploy your data project in no time!!! In fact all examples we will study will be ready to be deployed in real scenario!


You will learn R by practicing re-using provided material. More over you can easily retain and reuse the knowledge from the course – all lectures with code are available as downloadable html files.  You will get useful knowledge on Version Control to be super organized and productive.


Join this course to know how to take advantage and use Artificial Intelligence in Problem Solving

Goal of the Course

Introduction to the course
What we will use to learn

To know what you will use in the course

Introducing our case study

In this lecture you will get introduced to the case study we will be looking at the course and the final result we will going to get

How to get the most of this course

Quick intro on how to get the most of the course

A bit of theory

Ideas from Problem Solving

The theory about Problem Solving and how can we combine that with Artificial Intelligence

What is k-means?

To get intuitive understanding on what k-means clustering algorithm does


A bit of practice

Install R & R-Studio

How to install R and R-Studio IDE

Practice Creating your Project and ShinyApp

Learn to create your first projects in R. Also some little practice to code in ShinyApp

Get the code easy! A quick win!
Practical Activity: Set Up your own working Environment

Let's Make it Happen or How our ShinyApp work?!

Introduction to the chapter...

To know how to apply our method of Anomaly Detection directly in ShinyApp

User Interface of ShinyApp - build HTML with R functions
Server Part - Calling Data to ShinyApp

In this lecture we will discuss about three main ideas for data loading to the ShinyApp

Server Part - Manipulating Data in ShinyApp

Lecture explaining manipulation of data using Pipe

Using Interactive Inputs

Explained how to use inputs previously generated in ui.R inside server.R

Unsupervised Machine Learning

Manipulation of data and performing Unsupervised Machine Learning with kmeans function in R

Creating Dynamic Outputs
Creating user preferred layout

Your Project - New Data Set

Your Project - Introducing New Dataset

This lecture introduces industrial environment and the data coming from it

Your Project - apply method on other data!!!

In this lecture we will see in the detail how possible app can look like. Potential solution explained will not be the final one. You can decide to apply different strategies to solve a business problem - alert operators of the process on up-coming anomaly!!!

Your Project - Solution, use and new challenge!!!

This lecture is explaining one possible solution, covers possible use cases of this method in real industrial process. Setting up new challenges to achieve the same goal... feel free to contribute to Bonus Section of this course!!!

Other Options, including Deep Learning

Feature Engineering

Bonus lecture about how to perform "data massaging" before applying Unsupervised Machine Learning. We will be working with Time-Series data in R

Wavelet Analysis
Deep Learning Autoencoders in H20 - Install & Example

Deep Learning with H2O Machine Learning Platform. Part 1.

  1. Install it for R,
  2. Example on ECG dataset,
  3. Strategy for Time-Series Sensor Data
Deep Learning with H2O - Build Model on our data

This lecture is dedicated to:

  • Data preparation
  • Model Training
  • Model Testing
  • Saving Model
Deep Learning with H2O - Use Model to predict

Lecture that focuses on

  1. Load model from R Project Folder, use model to check for anomaly
  2. Discussion on possible user interface for anomaly detection or alert
  3. Implementing strategy of 'Anomaly Rating'
  4. Create function that transform matrices to the dataframe
Deep Learning with H2O - Put into production with ShinyApp

Detect Anomaly in Industrial Process with Deep Learning

Introducing the task and business need

Introducing our working environment and new Einstein's quote!

Selecting the Dataset

Explaining the dataset for this Section

Fitting and testing the Model
Demo ShinyApp

Setting up the demo shiny app. Train and save the model

Demo App in Action!

How to use this shiny app for demonstration purposes


Where to learn more and stay curious?

Additional article summarizing anomaly detection methods

What have you learnt?
Bonus Lecture Where to go from here?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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